Update legacy systems faster with AI automation tools

If your team is wasting time trying to understand and update 1990s code instead of building 2025 solutions, it might be time to have a conversation about codebase modernization. Luckily, AI can help.

Many enterprise companies continue operating with outdated IT infrastructure built decades ago. These aging systems create security risks, introduce software defects, and slow down development cycles, preventing teams from meeting delivery deadlines.

Modernizing these systems requires significant time and budget investments. Organizations recognize the long-term benefits of infrastructure upgrades, but justifying immediate costs proves difficult when returns may not appear for several years.

Memory-unsafe programming languages remain embedded within complex enterprise systems, creating ongoing security concerns. Research indicates that approximately 70% of security flaws stem from outdated systems using memory-unsafe languages. Legacy code presents challenges for developers who must understand and convert it to contemporary memory-safe alternatives or updated application frameworks. Successfully migrating these systems requires developers with expertise across multiple programming languages.

Proactive AI agents can assist development teams during modernization projects, enabling human developers to concentrate on strategic planning and building innovative customer solutions. Organizations can leverage AI for modernization through three primary approaches: explaining legacy programming languages, supporting new application development, and accelerating security issue resolution.

Explaining and updating legacy code

Standard refactoring methods include inline refactoring, which restructures outdated code components, and abstraction, which eliminates duplicate code. These traditional methods require substantial time, experienced developers knowledgeable in outdated languages, and comprehensive testing to ensure their effectiveness.

Junior development teams typically lack sufficient knowledge and background in the legacy languages found in existing codebases. This makes understanding legacy source code an overwhelming and lengthy process. AI agents can streamline this process by converting existing code into natural language and then creating updated code using memory-safe languages for review and testing by human developers.

The new code can then operate on cloud infrastructure using microservices or other available compute resources. This approach allows modernized code to deliver identical functionality with improved efficiency and security, plus enhanced scalability and faster response times.

Supporting new application development

In addition to modernizing existing code, AI can help create new applications based on specific requirements or business functions. Development teams can provide requirements using natural language descriptions, and AI can then generate frameworks and code components that support those needs, sometimes even writing substantial portions of applications using modern architectures.

AI can also aid collaboration among development professionals by summarizing feedback within code reviews, identifying potential integration problems, and tracking compliance requirements, making communication smoother across distributed development organizations.

Accelerating security assessment and issue resolution

Security responsibilities now extend beyond dedicated security professionals to include developers throughout the organization. Constant threats from malicious actors have grown substantially with AI-enhanced attack methods that exploit known weaknesses in legacy applications and aging infrastructure. Security teams must adopt AI tools to keep pace with these evolving threats.

AI can analyze existing code for user behavioral patterns, conduct root cause investigations, automate security assessments, and apply fixes for identified vulnerabilities. This improves coordination between security professionals and developers, enabling them to recognize and address security issues independently and reducing security team workloads.

This partnership between AI, developers, and security professionals has the potential to allow organizations to respond more quickly to emerging threats and cut response times from days to hours.

Building for tomorrow

Although the transition from legacy codebase maintenance to comprehensive modernization appears challenging, it represents an essential step for maintaining organizational security and preparing for the future. The bottom line is that development teams should focus their energy on delivering value to customers, not on supporting and maintaining outdated languages and frameworks. AI-powered code modernization helps teams optimize workflows, enhance performance, and encourage innovation while reducing operational expenses.

Modernizing legacy systems also eliminates entire categories of security vulnerabilities and strengthens protection across the technology landscape. AI will function as both an accelerator and a protector in this technological evolution.

Companies that embrace AI-driven modernization position themselves to compete more effectively while building stronger, more secure digital foundations for future growth and innovation.